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Adaptive Normalized Gradient Method for $(L_0,L_1)$-smooth Problems: Estimating Problem Parameters as a Region
We propose a parameter-free and efficient gradient descent algorithm for $(L_0,L_1)$-smooth optimization. This notion of smoothness, introduced by Zhang et al. (2020), allows the smoothness bound to grow with the gradient norm. Similar to cutting-plane localization, our method uses a sequence of obs
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